Classification of maize growth stages using deep neural networks with voting classifier

Deep learning significantly supports key tasks in science, engineering, and precision agriculture. In this study, we propose a method for automatically determining maize developmental stages on the BBCH scale (phases 10-19) using RGB and multispectral images, deep neural networks, and a voting classifier. The method was evaluated using RGB images and multispectral data from the MicaSense RedEdge MX-Dual camera, with training conducted on HTC_r50, HTC_r101, HTC_x101, and Mask2Former architectures. The models were trained on RGB images and separately on individual spectral channels from the multispectral camera, and their effectiveness was evaluated based on classification performance. For multispectral images, a voting classifier was employed because the varying perspectives of individual spectral channels made it impossible to align and merge them into a single coherent image. Results indicate that HTC_r50, HTC_r101, and HTC_x101 trained on spectral channels with a voting classifier outperformed their RGB-trained counterparts in precision, recall, and F1-score, while Mask2Former demonstrated higher precision with a voting classifier but achieved better accuracy, recall, and F1-score when trained on RGB images. Mask2Former trained on RGB images yielded the highest accuracy, whereas HTC_r50 trained on spectral channels with a voting classifier achieved superior precision, recall, and F1-score. This approach facilitates automated monitoring of maize growth stages and supports result aggregation for precision agriculture applications. It offers a scalable framework that can be adapted for other crops with appropriate labeled datasets, highlighting the potential of deep learning for crop condition assessment in precision agriculture and beyond.